Llama distillation notebook fixes (#3322)

* Adding missing datasets package

* Doc explaining TEACHER_MODEL_ENDPOINT_NAME and TEACHER_MODEL_NAME

* mlclient.models.get requires version or label

* data directory must be created prior to saving datasets

* Only one sample is saved in train and valid JSONL files

- was using `w` mode which replaces the file with each dataset JSON row, leaving only one sample at the end
- replaced `w` write mode with `a` append mode so that all JSON lines are saved to train and valid files

* Experiment name cannot contain `.`

- Llama 3.1 models have `.` in their names, thus failing the experiment name constraint of not having a `.`

* Printing studio progress URL

* Llama 3.1 marketplace subscription prerequisites

sdk/python/foundation-models/system/finetune/Llama-notebooks/distillation/distillation_chat_completion.ipynb

@@ -18,7 +18,10 @@
     "- Distillation should only be used for single turn chat completion format.\n",
     "- The Meta Llama 3.1 405B Instruct model can only be used as a teacher model.\n",
     "- The Meta Llama 3.1 8B Instruct can only be used as a student (target) model.\n",
-    "- Distllation is currently supported only for Natural Language Inference (NLI) task, which is a standard task in benchmarking for Natural Language Understanding."
+    "- Distllation is currently supported only for Natural Language Inference (NLI) task, which is a standard task in benchmarking for Natural Language Understanding.\n",
+    "\n",
+    "**Prerequisites :**\n",
+    "- Subscribe to the Meta Llama 3.1 405B Instruct and Meta Llama 3.1 8B Instruct, see [how to subscribe your project to the model offering in MS Learn](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/deploy-models-serverless?tabs=azure-ai-studio#subscribe-your-project-to-the-model-offering)"
    ]
   },
   {
@@ -38,7 +41,8 @@
     "# %pip install azure-identity\n",
     "\n",
     "# %pip install mlflow\n",
-    "# %pip install azureml-mlflow"
+    "# %pip install azureml-mlflow\n",
+    "# %pip install datasets"
    ]
   },
   {
@@ -163,8 +167,12 @@
    "outputs": [],
    "source": [
     "# Llama-3-405B Teacher model endpoint name\n",
+    "# The serverless model name is the name found in ML Studio > Endpoints > Serverless endpoints > Model column\n",
     "TEACHER_MODEL_NAME = \"Meta-Llama-3.1-405B-Instruct\"\n",
-    "TEACHER_MODEL_ENDPOINT_NAME = \"<Please provide Meta Llama 3.1 405B endpoint name>\""
+    "\n",
+    "# The serverless model endpoint name is the name found in ML Studio > Endpoints > Serverless endpoints > Name column\n",
+    "# The endpoint URL will be resolved from this name by the MLFlow component\n",
+    "TEACHER_MODEL_ENDPOINT_NAME = \"Meta-Llama-3-1-405B-Instruct-vum\""
    ]
   },
   {
@@ -183,10 +191,13 @@
    "outputs": [],
    "source": [
     "STUDENT_MODEL_NAME = \"Meta-Llama-3.1-8B-Instruct\"\n",
+    "STUDENT_MODEL_VERSION = 1\n",
     "\n",
     "# retrieve student model from model registry\n",
     "mlclient_azureml_meta = MLClient(credential, registry_name=\"azureml-meta\")\n",
-    "student_model = mlclient_azureml_meta.models.get(STUDENT_MODEL_NAME)\n",
+    "student_model = mlclient_azureml_meta.models.get(\n",
+    "    STUDENT_MODEL_NAME, version=STUDENT_MODEL_VERSION\n",
+    ")\n",
     "\n",
     "print(\n",
     "    \"\\n\\nUsing model name: {0}, version: {1}, id: {2} for fine tuning\".format(\n",
@@ -290,6 +301,15 @@
     "print(\"Len of validation data sample is \" + str(len(val)))"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! mkdir -p data"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -316,7 +336,7 @@
     "            + row[\"hypothesis\"],\n",
     "        }\n",
     "    )\n",
-    "    with open(train_data_path, \"w\") as f:\n",
+    "    with open(train_data_path, \"a\") as f:\n",
     "        f.write(json.dumps(data) + \"\\n\")\n",
     "\n",
     "for row in val:\n",
@@ -336,7 +356,7 @@
     "            + row[\"hypothesis\"],\n",
     "        }\n",
     "    )\n",
-    "    with open(valid_data_path, \"w\") as f:\n",
+    "    with open(valid_data_path, \"a\") as f:\n",
     "        f.write(json.dumps(data) + \"\\n\")"
    ]
   },
@@ -504,6 +524,7 @@
     "train_file_path_input = Input(type=\"uri_file\", path=train_data.path)\n",
     "validation_file_path_input = Input(type=\"uri_file\", path=valid_data.path)\n",
     "input_finetune_model = Input(type=\"mlflow_model\", path=student_model.id)\n",
+    "experiment_name = f\"distillation-{TEACHER_MODEL_NAME}\".replace(\".\", \"-\")\n",
     "\n",
     "finetuning_job = distillation_pipeline(\n",
     "    teacher_model_endpoint_name=TEACHER_MODEL_ENDPOINT_NAME,\n",
@@ -519,7 +540,7 @@
     "\n",
     "# pipeline_job.identity = UserIdentityConfiguration()\n",
     "finetuning_job.display_name = f\"finetune-{student_model.name}\"\n",
-    "finetuning_job.experiment_name = f\"distillation-{TEACHER_MODEL_NAME}\"\n",
+    "finetuning_job.experiment_name = experiment_name\n",
     "finetuning_job.settings.default_compute_type = \"serverless\"\n",
     "finetuning_job.continue_on_step_failure = False\n",
     "# pipeline_job.settings.force_rerun = True"
@@ -540,9 +561,7 @@
    "source": [
     "# Submit pipeline job to workspace\n",
     "ft_job = ml_client.jobs.create_or_update(finetuning_job)\n",
-    "# ft_job.studio_url\n",
-    "\n",
-    "# build link to ai studio fine-tuning tab"
+    "print(f\"Submitted job, progress available at {ft_job.studio_url}\")"
    ]
   },
   {